May 4, 2020

The triumph of Google’s AlphaGo in 2016 against Go world champion Lee Sedol by 4:1 caused quite the stir that reached far beyond the Go community, with over a hundred million people watching while the match was taking place. It was a milestone in the development of AI: Go had withstood the attempts of computer scientists to build algorithms that could play at a human level for a long time. And now an artificial mind had been built, dominating someone that had dedicated thousands of hours of practice to hone his craft with relative ease.

This was already quite the achievement, but then AlphaGoZero came along, and fed AlphaGo some of its own medicine: it won against AlphaGo with a margin of 100:0 only a year after Lee Sedol’s defeat. This was even more spectacular, and for more than the obvious reasons. AlphaGoZero was not only an improved version of AlphaGo. Where AlphaGo had trained with the help of expert games played by the best human Go players, AlphaGoZero had started literally from zero, working the intricacies of the game out without any supervision.

May 4, 2020

The world’s largest meat processing company begins experimenting with machine learning in their plants. Developing and implementing these smart machines, capable of performing skilled and dexterous tasks, is pushing the current boundaries of automation.

JBS is the world’s largest meat processing company. With revenues of over $51 billion, it operates over 300 production units worldwide specializing in the processing of pork, beef, poultry, and lamb [1]. As meat and protein remain a mostly commoditized industry, JBS continually strives to maximize efficiency in all aspects of the value chain. To increase its processing efficiencies and worker safety, JBS bought a controlling share of New Zealand based Scott Technology, an automation and robotics company in late 2015 [2]. This move accelerated the implementation of machine learning in meat processing plants.

May 2, 2020

With the trillions of antibodies the human body can make, finding the antibody with the right combination of potency against a target and ease of manufacturing is, at best, an arduous, time-intensive endeavor for drug developers. AbCellera Biologics Inc. has developed a way to dramatically speed that process.

It is using its proprietary AI system to empower the search. It is mining the diversity of antibodies made by the immune system to find the relatively few that are optimized by nature to be well-suited for drug development. “A human makes trillions of different antibodies, but only a small set binds to the target of interest. Of those, only a few can be developed as drugs,” Carl Hansen, Ph.D., CEO of AbCellera, explained.

Hansen sees AbCellera as a “discovery and innovation shop. We identify the properties of antibodies that make them easy to manufacture and potent.”

May 2, 2020

There’s been a lot of talk about quantum computers being able to solve far more complex problems than conventional supercomputers. The authors of a new paper say they’re on the pat h to showing an optical computer c an do so, too.

The idea of using light to carry out computing has a long pedigree, and it has gained traction in recent years with the advent of silicon photonics, which makes it possible to build optical circuits using the same underlying technology used for electronics. The technology s hows particular promise for accelerating deep learning, and is being actively pursued by Intel and a number of startups.

Now Chinese researchers have put a photonic chip t o work tackling a fiendishly complex computer science challenge called the s ubset sum problem in a paper in Science Advances. It ha s some potential applications in cryptography and resource allocation, but primarily it’s used as a benchmark to test the limits of computing.

May 2, 2020

Researchers at the University of Massachusetts and the Air Force Research Laboratory Information Directorate have recently created a 3D computing circuit that could be used to map and implement complex machine learning algorithms, such convolutional neural networks (CNNs). This 3D circuit, presented in a paper published in Nature Electronics, comprises eight layers of memristors; electrical components that regulate the electrical current flowing in a circuit and directly implement neural network weights in hardware.

“Previously, we developed a very reliable memristive device that meets most requirements of in-memory computing for artificial neural networks, integrated the devices into large 2-D arrays and demonstrated a wide variety of machine intelligence applications,” Prof. Qiangfei Xia, one of the researchers who carried out the study, told TechXplore. “In our recent study, we decided to extend it to the third dimension, exploring the benefit of a rich connectivity in a 3D neural network.”

Essentially, Prof. Xia and his team were able to experimentally demonstrate a 3D computing circuit with eight memristor layers, which can all be engaged in computing processes. Their circuit differs greatly from other previously developed 3D circuits, such as 3D NAND flash, as these systems are usually comprised of layers with different functions (e.g. a sensor layer, a computing layer, a control layer, etc.) stacked or bonded together.

May 2, 2020

The news: In a fresh spin on manufactured pop, OpenAI has released a neural network called Jukebox that can generate catchy songs in a variety of different styles, from teenybop and country to hip-hop and heavy metal. It even sings—sort of.

How it works: Give it a genre, an artist, and lyrics, and Jukebox will produce a passable pastiche in the style of well-known performers, such as Katy Perry, Elvis Presley or Nas. You can also give it the first few seconds of a song and it will autocomplete the rest.